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Performance of RNN encoders (unidirectional) are \(\text{Jordan}\le \text{Elman}<\text{LSTM}\) . Bi-directional encoding is additive to the performance of each encoder.

Incorporating more context information is better for SF performance. Using global context information, such as sentence level representation, and attention mechanisms [1]}, [2]} boosts performance of bi-directional encoder even further.

When adding external features is possible, semantic features such as NE are more beneficial than syntactic features for SF. When NE is used, it can boost the model performance for SF significantly.

The slot filling task is related to Named Entity Recognition (NER) [3]} task as slot values can be a named entity such as airline name, city name etc. If the slot filling task is modeled as a sequence tagging problem, basically recent neural models proposed for NER can be used for slot filling and vice versa. To know more about the recent development of neural NER models, one can consult the survey from yadav-bethard-2018-survey.

The main disadvantage of independent models is that they do not exploit the interaction between intent and slots and may introduce error propagation when they are used in a pipeline.

